{"title":"The impact of the change in the splitting method of decision trees on the prediction power","authors":"Youngjae Chang","doi":"10.5351/kjas.2022.35.4.517","DOIUrl":"https://doi.org/10.5351/kjas.2022.35.4.517","url":null,"abstract":"","PeriodicalId":43523,"journal":{"name":"Korean Journal of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44854481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A study on time series linkage in the Household Income and Expenditure Survey","authors":"Sihyeon Kim, B. Seong, Young-Geun Choi, I. Yeo","doi":"10.5351/kjas.2022.35.4.553","DOIUrl":"https://doi.org/10.5351/kjas.2022.35.4.553","url":null,"abstract":"","PeriodicalId":43523,"journal":{"name":"Korean Journal of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44961949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Time series analysis for Korean COVID-19 confirmed cases: HAR-TP-T model approach","authors":"S. Yu, E. Hwang","doi":"10.5351/kjas.2021.34.2.239","DOIUrl":"https://doi.org/10.5351/kjas.2021.34.2.239","url":null,"abstract":"This paper studies time series analysis with estimation and forecasting for Korean COVID-19 confirmed cases, based on the approach of a heterogeneous autoregressive (HAR) model with two-piece t (TP-T) distributed errors. We consider HAR-TP-T time series models and suggest a step-by-step method to estimate HAR coefficients as well as TP-T distribution parameters. In our proposed step-by-step estimation, the ordinary least squares method is utilized to estimate the HAR coefficients while the maximum likelihood estimation (MLE) method is adopted to estimate the TP-T error parameters. A simulation study on the step-by-step method is conducted and it shows a good performance. For the empirical analysis on the Korean COVID-19 confirmed cases, estimates in the HAR-TP-T models of order p = 2, 3, 4 are computed along with a couple of selected lags, which include the optimal lags chosen by minimizing the mean squares errors of the models. The estimation results by our proposed method and the solely MLE are compared with some criteria rules. Our proposed step-by-step method outperforms the MLE in two aspects: mean squares error of the HAR model and mean squares difference between the TP-T residuals and their densities. Moreover, forecasting for the Korean COVID-19 confirmed cases is discussed with the optimally selected HAR-TP-T model. Mean absolute percentage error of one-step ahead out-of-sample forecasts is evaluated as 0.0953% in the proposed model. We conclude that our proposed HAR-TP-T time series model with optimally selected lags and its step-by-step estimation provide an accurate forecasting performance for the Korean COVID-19 confirmed cases.","PeriodicalId":43523,"journal":{"name":"Korean Journal of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71084380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Statistical Considerations in the Design of Biosimilar Cancer Clinical Trials.","authors":"Chul Ahn, Seung-Chun Lee","doi":"10.5351/KJAS.2011.24.3.495","DOIUrl":"https://doi.org/10.5351/KJAS.2011.24.3.495","url":null,"abstract":"<p><p>When the patent of an innovative (brand-name) small-molecule drug expires, generic copies of the innovative drug may be marketed if their therapeutic equivalence to the innovative drug has been shown. The small-molecule drugs are considered therapeutically equivalent and can be used interchangeably if two drugs are shown to be pharmaceutically equivalent with identical active substance and bioequivalent with comparable pharmacokinetics in a crossover clinical trial. However, the therapeutic equivalence paradigm cannot be applied to biosimilars since the active ingredients of biosimilars are huge molecules with complex and heterogeneous structures, and these molecules are difficult to replicate in every detail. The European Medicine Agency (EMEA) has introduced a regulatory biosimilar pathway which mandates clinical trials to show therapeutic equivalence. In this paper, we discuss statistical considerations in the design and analysis of biosimilar cancer clinical trials.</p>","PeriodicalId":43523,"journal":{"name":"Korean Journal of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2011-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3691070/pdf/nihms345674.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31537662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}